Traditional JPEG series image compression algorithms have limitations in speed. To improve the storage and transmission of 14-bit/pixel images acquired by infrared line-scan detectors, a novel method is introduced for achieving high-speed and highly efficient compression of line-scan infrared images. The proposed method utilizes the features of infrared images to reduce image redundancy and employs improved Huffman coding for entropy coding. The improved Huffman coding addresses the low-probability long coding of 14-bit images by truncating long codes, which results in low complexity and minimal loss in the compression ratio. Additionally, a method is proposed to obtain a Huffman code table that bypasses the pixel counting process required for entropy coding, thereby improving the compression speed. The final implementation is a low-complexity lossless image compression algorithm that achieves fast encoding through simple table lookup rules. The proposed method results in only a 10% loss in compression performance compared to JPEG 2000, while achieving a 20-fold speed improvement. Compared to dictionary-based methods, the proposed method can achieve high-speed compression while maintaining high compression efficiency, making it particularly suitable for the high-speed, high-efficiency lossless compression of line-scan panoramic infrared images. The code table compression effect is 5% lower than the theoretical value. The algorithm can also be applied to analyze images with more bits.

Gain and bias non-uniformities in infrared line-scanning detectors often result in horizontal streak noise, degrading image quality. This paper introduces a novel non-uniformity correction algorithm combining residual guidance and adaptive weighting, which achieves superior denoising and detail preservation compared to existing methods. The method combines residual and original images in a dual-guidance mechanism and significantly enhances denoising performance and detail preservation through iterative compensation strategies and locally weighted linear regression. Additionally, the algorithm employs local variance to adjust weights dynamically, achieving efficient correction in complex scenes while reducing computational complexity to meet real-time application requirements. Experimental results on both simulated and real infrared datasets demonstrate that the proposed method outperforms mainstream algorithms regarding peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, achieving an optimal balance between detail preservation and noise suppression. The algorithm demonstrates robust performance in complex scenes, making it suitable for real-time applications in high-resolution infrared imaging systems.

Stripe noise along the scanning direction significantly degrades the quality of high-resolution infrared line-scan images and impairs downstream tasks such as target detection and radiometric analysis. This paper presents a lightweight, single-frame, reference-free non-uniformity correction (NUC) method tailored for such images. The proposed approach enhances the directionality of stripe noise by projecting the 2D image into a 1D row-mean signal, followed by adaptive guided filtering driven by local median absolute deviation (MAD) to ensure spatial adaptivity and structure preservation. A spectral-entropy-constrained frequency-domain masking strategy is further introduced to suppress periodic and non-periodic interference. Extensive experiments on simulated and real datasets demonstrate that the method consistently outperforms six state-of-the-art algorithms across multiple metrics while maintaining the fastest runtime. The proposed method is highly suitable for real-time deployment in airborne, satellite-based, and embedded infrared imaging systems. It provides a robust and interpretable framework for future infrared enhancement tasks.

Infrared line-scanning images have high redundancy and large file sizes. In JPEG2000 compression, the MQ arithmetic encoder’s complexity slows down processing. Huffman coding can achieve O(1) complexity based on a code table, but its integer-bit encoding mechanism and ignorance of the continuity of symbol distribution result in suboptimal compression performance. In particular, when encoding sparse quantized wavelet coefficients that contain a large number of consecutive zeros, the inaccuracy of the one-bit shortest code accumulates, reducing compression efficiency. To address this, this paper proposes Huf-RLC, a Huffman-based method enhanced with Run-Length Coding. By leveraging zero-run continuity, Huf-RLC optimizes the shortest code encoding, reducing the average code length to below one bit in sparse distributions. Additionally, this paper proposes a wavelet coefficient probability model to avoid the complexity of calculating statistics for constructing Huffman code tables for different wavelet subbands. Furthermore, Differential Pulse Code Modulation (DPCM) is introduced to address the remaining spatial redundancy in the low-frequency wavelet subband. The experimental results indicate that the proposed method outperforms JPEG in terms of PSNR and SSIM, while maintaining minimal performance loss compared to JPEG2000. Particularly at low bitrates, the proposed method shows only a small gap with JPEG2000, while JPEG suffers from significant blocking artifacts. Additionally, the proposed method achieves compression speeds 3.155 times faster than JPEG2000 and 2.049 times faster than JPEG.

To enhance scene perception and comprehension, infrared and visible image fusion (IVIF) integrates complementary data from two modalities. However, many existing methods fail to explicitly separate modality-specific and modality-shared features, which compromises fusion quality. To surmount this constraint, we introduce a novel hierarchical dual-branch fusion (HDF-Net) network. The network decomposes the source images into low-frequency components, which capture shared structural information, and high-frequency components, which preserve modality-specific details. Remarkably, we propose a pin-wheel-convolutional transformer (PCT) module that integrates local convolutional processing with directional attention to improve low-frequency feature extraction, thereby enabling more robust global–local context modeling. We subsequently introduce a hierarchical feature refinement (HFR) block that adaptively integrates multiscale features using kernel-based attention and dilated convolutions, further improving fusion accuracy. Extensive experiments on four public IVIF datasets (MSRS, TNO, RoadScene, and M3FD) demonstrate the high competitiveness of HDF-Net against 12 state-of-the-art methods. On the RoadScene dataset, HDF-Net achieves top performance across six key metrics—EN, SD, AG, SF, SCD, and SSIM—surpassing the second-best method by 0.67%, 1.85%, 17.67%, 5.26%, 3.33%, and 1.01%, respectively. These findings verify the generalization and efficacy of HDF-Net in practical IVIF scenarios.