In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We illustrate that such priors can be identified into a generalized $L_{0}$ -norm minimization problem in higher-order gradient domains, giving rise to a new “feature-aware” filter with a powerful simultaneous-fitting ability in both sparse singularities (corners and salient edges) and polynomial-smoothing surfaces. Notice that a direct solver to the proposed model is not available due to the non-convexity and combinatorial nature of $L_{0}$ -norm minimization. Instead, we propose to solve it approximately based on an efficient half-quadratic splitting technique. We demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications.

This article presents a novel intrinsic image transfer (IIT) algorithm for image illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework composed of illumination, reflectance and content photo-realistic losses, respectively. Each loss is first defined on the corresponding sub-layers factorized by an intrinsic image decomposition and then reduced under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. We illustrate that all losses, with the aid of an “exemplar” image, can be directly defined on images without the necessity of taking an intrinsic image decomposition, thereby giving a closed-form solution to image illumination manipulation. We also demonstrate its versatility and benefits to several illumination-related tasks: illumination compensation, image enhancement and tone mapping, and high dynamic range (HDR) image compression, and show their high-quality results on natural image datasets.

Transformer-based deep learning techniques have recently shown outstanding potential in remote sensing scene classification (RSSC), benefiting from their ability to capture global semantic relationships and contextual dependencies. However, effectively utilizing the raw image and global semantic information while simultaneously taking into account detailed features and multi-scale spatial relationships remains a major challenge. Therefore, this paper proposes a novel FG-Swin KANsformer model that integrates frequency domain and gradient prior information from raw images with the Kolmogorov–Arnold Network (KAN) to enhance nonlinear feature modeling. The FG-Swin KANsformer consists of three key components: the Discrete Cosine Transform (DCT) module, the gradient-spatial feature extraction (GSFE) module, and the Swin Transformer module integrated with KAN. In the feature embedding phase, the DCT module extracts frequency domain features, while the GSFE module uses multi-scale convolutions and Sobel operators to extract spatial structures and gradient information at different scales, thereby enhancing the utilization of the original image’s frequency domain and gradient prior information. In the Swin Transformer feature modeling phase, the conventional multilayer perceptron (MLP) in Swin Transformer Blocks is replaced by KAN, which decomposes complex multivariate functions into univariate compositions, thereby improving nonlinear representation capacity and enhancing feature discrimination. The thorough experiments on three distinct public remote sensing (RS) datasets demonstrate that FG-Swin KANsformer exhibits outstanding performance.

This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intrinsic image decomposition. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. Moreover, with a series of relaxations, all of them can be directly defined on images, giving a closed-form solution for image illumination manipulation. This new paradigm differs from the prevailing Retinex-based algorithms, as it provides an implicit way to deal with the per-pixel image illumination. We finally demonstrate its versatility and benefits to the illumination-related tasks such as illumination compensation, image enhancement, and high dynamic range (HDR) image compression, and show the high-quality results on natural image datasets.