Image Enhancement Method Based on Bilinear Interpolating and Wavelet Transform

In this paper, a new method for color image enhancement based on wavelet transform is presented. The three different colour channels such as R, G, and B of the input color image are separated initially. The proposed technique decomposes the different colour channels into the four frequency sub-bands by using Discrete Wavelet Transform (DWT). Histogram Equalization is applied to the low frequency sub-band only. Finally, the inverse wavelet transform is applied to reconstruct the enhanced image. Mean is the parameter which is used to measure the performance of the proposed method.

Image interpolation is used widely in the computer vision. Holding edge information is main problem in the image interpolation. By using bilinear and bicubic B-spline interpolation methods, a novel image interpolation approach was proposed in this paper. Firstly, inverse distance weighted average method was used to reduce image’s noise. Secondly, edge detection operator was used to extract image's edges information. It can help us to select different interpolation methods in the image interpolation process. Finally, we selected bilinear interpolation approach at non-edge regions, and bicubic B-spline interpolation method was used near edges regions. Further more, control vertexes were computed from pixels with calculation formula which has been simplified in the B-spline interpolation process. Experiments showed the interpolated image by the proposed method had good vision results for it could hold image's edge information effectively.

In most application of diagnostic imaging the amount of digitized information (up to 4096 grey levels per pixel) cannot be detected by the human observer as a whole, so that it is common practice to interactively select a suitable intensity window for the display, sometimes with saturation effects. To overcome this problem many different contrast stretching approaches have been proposed, to almost equalize the diagnostic information on the image. The solution proposed here is based on a non-linear transformation, acting as an adaptive histogram equalization of the local differences, in the hypothesis of gaussian distributions. The obtained results are closely related to other enhancement methods, with some advantage in the look-up filter implementation. The computational cost of the realization is evaluated, as well as the loss of performance determined by suboptimal block-processing and bilinear interpolation. Examples are referred in the contrast enhancement of C.T. images and digitized X-Rays.

This paper presents a novel super-resolution image restoration algorithm based on Partial Least Squares regression and the Wavelet Bi-cubic ratio interpolation. By wavelet transform, the original low-resolution image can be decomposed into high-frequency and low-frequency sub-images. Before reconstructing images by inverse wavelet transformation, high-frequency sub-images can be interpolated according to the bi-cubic interpolation algorithm. At the same time, the high-resolution image can be obtained from the original low-resolution image by bi-cubic interpolation. Firstly, fuse the high-resolution image with reconstructed image by inverse wavelet transformation. Then, take the fused image as training samples to restore original images by partial least squares method. At last, we compare these results, which are obtained from the algorithms based on the interpolation (such as the nearest neighbor interpolation, the bilinear interpolation, the bi-cubic interpolation and so on). The experimental data shows that the super-resolution restoration algorithm based on partial least squares and wavelet bi-cubic ratio interpolation algorithm can get better effect than the traditional algorithms.

Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of the data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the ‘‘seed’’ when being processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are ‘‘similar’’ to the seed. Then, the histogram of the region is stretched to a range that is computed with respect to the statistical parameters of the region (mean and variance) and to the global HE function (of intensities), and only the seed pixel is given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively ‘‘pleasant to the human eye,’’ with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably with those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.

In this paper, a new method has been proposed for low contrast image enhancement based on the lifting discrete wavelet transform and singular value decomposition. The given input image decomposes in four subbands with lifting discrete wavelet transform scheme. Then, we estimate the singular value matrix of the low-low subband image and by applying inverse lifting wavelet transform reconstruct enhanced image. The proposed method also compared with the existing traditional methods like general histogram equalization, local histogram equalization and dynamic histogram equalization. The method is tested on different images. The results show that proposed method is batter then existing methods.

According to features of the human finger vein images, the algorithm of image enhancement based on the Ridgelet transformation is presented in this paper. Firstly the high-frequency sub-band coefficients are transformed by Ridgelet after the finger vein image's wavelet transformation. Then the Ridgelet coefficients are processed by using the non-linear new coefficient-determined method. At last do the inverse Ridgelet transformation for each high-frequency sub-band and the Wavelet reconstruction. The algorithm is effective in processing the finger vein's two-dimensional curve singularity and the image edge enhancement. It overcomes the shortages of Wavelet transformation in high-dimensional curve singularity and direction choices. Comparing with the traditional two-dimensional discrete wavelet transformation and the algorithm presented in [1], the experimental results show that the algorithm presented in this paper has a better effect in finger vein's image enhancement.

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.

Whole-body bone scan is one of the most frequent diagnostic procedures in nuclear medicine. Especially, it plays a significant role in important procedures such as the diagnosis of osseous metastasis and evaluation of osseous tumor response to chemotherapy and radiation therapy. It can also be used to monitor the possibility of any recurrence of the tumor. However, it is a very time-consuming effort for radiologists to quantify subtle interval changes between successive whole-body bone scans because of many variations such as intensity, geometry, and morphology. In this paper, we present the most effective method of image enhancement based on histograms, which may assist radiologists in interpreting successive whole-body bone scans effectively. Forty-eight successive whole-body bone scans from 10 patients were obtained and evaluated using six methods of image enhancement based on histograms: histogram equalization, brightness-preserving bi-histogram equalization, contrast-limited adaptive histogram equalization, end-in search, histogram matching, and exact histogram matching (EHM). Comparison of the results of the different methods was made using three similarity measures peak signal-to-noise ratio, histogram intersection, and structural similarity. Image enhancement of successive bone scans using EHM showed the best results out of the six methods measured for all similarity measures. EHM is the best method of image enhancement based on histograms for diagnosing successive whole-body bone scans. The method for successive whole-body bone scans has the potential to greatly assist radiologists quantify interval changes more accurately and quickly by compensating for the variable nature of intensity information. Consequently, it can improve radiologists' diagnostic accuracy as well as reduce reading time for detecting interval changes.

According to the local characteristics of image similarity and transitivity, self-similarity and scale invariance characteristic of fractal characteristics, use fractal coding as the characteristic of similarity evaluation criteria, search internal image blocks and use the low resolution image analogy fusion in a single image to reconstruct a high resolution image information blocks, implement super-resolution enhancement based on fractal coding. The experimental results show that the fractal coding method can keep the image details better, the reconstruction error is smaller, is better than the bilinear interpolation and bicubic interpolation method.

A new technique for image contrast enhancement is proposed in this paper. The technique is based on the variational mode decomposition (VMD) of image and various histogram equalization approaches. The original image is decomposed into different sub-images or modes using VMD. The histogram equalization approaches such as the conventional histogram equalization, the contrast limited adaptive histogram equalization are applied to the mode1 of the original image. For obtaining the enhanced image, a weighting scheme is introduced to combine the processed mode1 image and the original image. The proposed technique is evaluated using various images including the medical image. The experimental results demonstrate that the proposed technique outperforms the existing methods for image enhancement. For medical images, the PSNR and the image quality index values improve by using the proposed technique over various HE approaches.

Histogram equalization is an important image enhancement technique widely used in image processing. It is good method of image enhancement indeed; but it doesn't consider about human visual response to light. We know that human visual response to light is non-linear, which is somewhat likes natural logarithmic function. So the gray levels of histogram equalized, image is not really equally distributed for human eyes. In fact, the ordinary histogram equalization is not visually equalized. According to the human visual model, this article puts forward a new algorithm or image histogram modification, with which modified image has visually equal gray level distribution. This algorithm is conducted while assuming exiting a visual image whose histogram can be counted and is equal distributed. Experiment proves that this algorithm is better than ordinary image histogram equalization technique in image enhancement. It fits human visual response well. It can be widely used in digital image processing.

Satellite imagery is employed in many different fields of study. These pictures have serious quality problems. Image enhancement algorithms, however, can improve it in terms of contrast, brightness, feature elimination from noise contents, etc. These algorithms present and analyse the picture's properties by sharpening, focusing, or smoothing the image. Therefore, the specific application determines the goal of picture enhancement. This paper briefly overviews picture enhancement methods that produce optimal and progressive outcomes for satellite images used for secured remote sensing. To do this, various image enhancement techniques are used, which are widely used today to improve image quality across various image processing applications. Some commonly used image enhancement techniques include spatial filtering, contrast stretching, and histogram equalisation. These techniques aim to enhance the visual quality of satellite images by adjusting brightness and contrast and reducing noise. These methods can also improve the interpretability of the images for remote sensing purposes. The enhancement of satellite images finds use in several fields, particularly security. It is essential for security applications, including threat detection, border control, and surveillance. Security professionals may more effectively analyse and understand data to spot any dangers or questionable activity by boosting the visual details and general quality. Keywords: Satellite image analysis; mean filter; secured application; SVM; wavelet transformation

Edge-based image interpolation often leads to an image with good quality because of the importance of sharp edges and smooth contours to the human vision. The wavelet-based image interpolation approach has good potential in producing interpolated images with high quality edges. Using the wavelet multiresolution analysis theory, such method is computationally demanding. In this paper, a new edge-based image interpolation method that uses symmetric biorthogonal wavelet transform is proposed. We form a list of ideal step edges and study the relationships between the wavelet approximation sub-image of each edge and its wavelet detail sub-images. Based on these relationships, a simple and efficient algorithm that predicts the edge information of high resolution images is proposed. For this method, the 9/7-M inverse wavelet transform is shown, experimentally, to yield better image interpolation performance compared to traditional image interpolation approaches

Face detection is a common computer technology being used in human identification applications. It can also refer to the process of locating human faces in a visual scene. Face detection is a branched field of object detection where all objects in an image are detected including several classes like cars, trees, humans… etc. Also face detection problems branch into a lot of cases, some focus on frontal faces, others focus on side pose and so on. In this paper, a new face detection method based on Bilinear Interpolation image zooming method and image enhancement by Adaptive Histogram Equalization (AHE) method is proposed. The new method gives an encouraging results for crowded human images. By comparing the proposed method with the Viola-Jones algorithm, face detector using the cascade object detector, which supported in MATLAB, the new method gives excellent results in detecting human faces with different resolutions, poses and sizes. It succeeds in detecting most of the human faces in the tested images regardless of image sizes. The new method is tested on several images in Pratheepan dataset with crowded humans. Also, I tested the new method on many images collected from the Internet, whose can be classified as crowded human images. Experimental results show that the proposed Ad_L_Hist method is more efficient in detecting human faces in crowded human images.