All Research Papers Published In Signal Processing: Image Communication

Research Article
10.1016/j.image.2026.117552

Hyperspectral and multispectral image fusion via N-gram transformer and local adaptive fusion strategy

Jul 01, 2026
Signal Processing: Image Communication
Yuyuan Luo + 1 more +1

Research Article
10.1016/j.image.2026.117506

AMS: Attention Map Seeds for enhancing interactive segmentation

Apr 01, 2026
Signal Processing: Image Communication
Qingsong Lv + 4 more +4

Research Article
10.1016/j.image.2026.117510

Inpainting-assisted reversible authentication method for demosaiced image with enhanced recoverability

Apr 01, 2026
Signal Processing: Image Communication
Wien Hong + 2 more +2

Research Article
10.1016/j.image.2026.117577

Low Light Image Enhancement and Semantic Segmentation Based On Axis Cross-Transformer and Optimized Deep Double Convolutional U-Net Model

Apr 01, 2026
Signal Processing: Image Communication
Chandu Dajiba Vaidya + 2 more +2

Research Article
10.1016/j.image.2026.117491

Global and interactive graph channel attention for robust stereo matching

Apr 01, 2026
Signal Processing: Image Communication
Jun Yu + 4 more +4

Research Article
10.1016/j.image.2026.117481

Secure vision: Integrated anti-spoofing and deep-fake detection system using knowledge distillation approach

Apr 01, 2026
Signal Processing: Image Communication
K Jayashree + 6 more +6

Research Article
1
10.1016/j.image.2026.117492

Enhancing few-shot semantic segmentation in remote sensing through magnitude-based pruning

Apr 01, 2026
Signal Processing: Image Communication
Kingsley Amoafo + 2 more +2

Research Article
10.1016/j.image.2025.117458

Multi-Frame Adaptive Image Enhancement Algorithm for vehicle-mounted dynamic scenes

Mar 01, 2026
Signal Processing: Image Communication
Jing Li + 4 more +4

Research Article
10.1016/j.image.2026.117527

EHIES-ECCCA: An efficient hybrid image encryption scheme using ECC and Cellular Automata with Secure Shared Key Generation

Feb 01, 2026
Signal Processing: Image Communication
Biswarup Yogi + 1 more +1

Research Article
10.1016/j.image.2025.117432

Rank-based transformation algorithm for image contrast adjustment

Feb 01, 2026
Signal Processing: Image Communication
Cheng-Hui Chen + 1 more +1

Performing proper image contrast adjustment without information loss is an art. Many adjustment methods are used. The default settings are often inappropriate for the image in question rendering a contrast adjustment depending on trial and error. We propose a simple method, rank-based transformation (RBT), for image contrast adjustment that requires no prior knowledge. This makes RBT an ideal first tool to apply for underexposed images. The RBT algorithm normalizes and equalizes all the intensity differences of the image over the full intensity range of the image data type, and thus assigning equal weight to all gradients. Even the state-of-the-art AI tool Cellpose visually benefits from RBT preprocessing. Our comparison of histogram normalization methods demonstrates the ability of RBT to bring out image features. • Contrast adjustment using Rank-Based Transformation (RBT) without prior knowledge. • Enhances underexposed images and improves performance of AI tools such as Cellpose. • Outperforms conventional global histogram normalization in revealing image features.