Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. These designs typically fail to preserve fine textures and structural fidelity in complex HDR scenes. Furthermore, most methods lack an effective mechanism to jointly model global tone consistency and local contrast enhancement, leading to globally flat or locally inconsistent outputs such as halo artifacts. We present the Differential Pyramid Representation Network (DPRNet), an end-to-end framework for high-fidelity tone mapping. At its core is a learnable differential pyramid that generalizes traditional Laplacian and Difference-of-Gaussian pyramids through content-aware differencing operations across scales. This allows DPRNet to adaptively capture high-frequency variations under diverse luminance and contrast conditions. To enforce perceptual consistency, DPRNet incorporates global tone perception and local tone tuning modules operating on downsampled inputs, enabling efficient yet expressive tone adaptation. Finally, an iterative detail enhancement module progressively restores the full-resolution output in a coarse-to-fine manner, reinforcing structure and sharpness. Experiments show that DPRNet achieves state-of-the-art results, improving PSNR by **2.39 dB** on the 4K HDR+ dataset and **3.01 dB** on the 4K HDRI Haven dataset, while producing perceptually coherent, detail-preserving results. Demo available at [DPRNet](https://xxxxxxdprnet.github.io/DPRNet/).

This paper focuses on extending the weighted median for use with multidimensional (multichannel) signals. Sorting multicomponent (vector) values and selecting the middle value is not well defined as in the scalar case. This paper introduces two median based multivariate filtering structures inspired by ML estimates of location in multivariate spaces. Unlike Astola's weighted vector median filter, the multichannel weighted median filter structures introduced in this paper are able to exploit the spatial and cross-channel correlations embedded in the data. Adaptive optimization algorithms for the filters are derived. The effectiveness of these algorithms is shown through image and array processing experiments.

In this paper, we show that the optimization needed to solve the least absolute deviations (LAD) regression problem can be viewed as a sequence of maximum likelihood estimates (MLE) of location. The derived algorithm reduces to an iterative procedure where a simple coordinate transformation is applied during each iteration to direct the optimization procedure along edge lines of the cost surface, followed by a MLE estimate of location which is executed by a weighted median operation. Requiring weighted medians only, the new algorithm can be easily modularized for hardware implementation, as opposed to most of the other existing LAD methods which require complicated operations such as matrix entry manipulations. The new algorithm provides a better trade-off solution between convergence speed and implementation complexity compared to existing algorithms.

Weighted median (WM) filtering structures for complex-valued samples have been proposed but none of them allows the use of complex-valued weights. This paper defines complex-valued weighting for median filters with complex-valued input samples. Two different approaches to the handling of weights in the complex domain are presented, both derived from characteristics of complex-valued linear filters, resulting in two definitions of the complex weighted median filter. The LMS optimizations of the proposed filtering schemes are also presented. Simulations are shown illustrating the performance of the new complex WM filter structures compared with previous approaches to the problem and with classical linear filters.