Despite advances in ultrathin cutting, serial sections in electron microscopy (EM) still exhibit noticeable anisotropy, with much lower z-axis resolution compared with the other two axes. As a result, the imaged biovolume suffers from low connectivity smoothness in contextual structures, which makes the subcellular ultrastructural segmentation challenging. The recent 2.5D hybrid convolutions allow the direct learning of asymmetric semantics from anisotropic features. However, plain representations without the isotropic scale prior limit the performance of the upper bound. This paper presents a novel framework, referred to as ReIsoSeg, which aims at incorporating an isotropic scaling prior into anisotropic biovolumes. More precisely, ReIsoSeg consists of an anisotropic primary encoder, a pseudo-isotropic auxiliary module, and a weight-shared decoder. The auxiliary module implicitly deforms the anisotropic features from the primary encoder to align with the isotropic prior. The re-isotropic loss squeezes the pseudo-isotropic representations into the anisotropic space to reuse the anisotropic labels. The shared decoder ensures that the outputs of the anisotropic encoder converge towards the isotropic representations. During the inference process, the auxiliary module is excluded. Comprehensive experiments were conducted on the AC3/AC4, CREMI, and MitoEM subcellular ultrastructure datasets. The obtained results demonstrate the high performance of the proposed ReIsoSeg.

RAW to sRGB mapping, which aims to convert RAW images from smartphones into RGB form equivalent to that of Digital Single-Lens Reflex (DSLR) cameras, has become an important area of research. However, current methods often ignore the difference between cell phone RAW images and DSLR camera RGB images, a difference that goes beyond the color matrix and extends to spatial structure due to resolution variations. Recent methods directly rebuild color mapping and spatial structure via shared deep representation, limiting optimal performance. Inspired by Image Signal Processing (ISP) pipeline, which distinguishes image restoration and enhancement, we present a novel Neural ISP framework, named FourierISP. This approach breaks the image down into style and structure within the frequency domain, allowing for independent optimization. FourierISP is comprised of three subnetworks: Phase Enhance Subnet for structural refinement, Amplitude Refine Subnet for color learning, and Color Adaptation Subnet for blending them in a smooth manner. This approach sharpens both color and structure, and extensive evaluations across varied datasets confirm that our approach realizes state-of-the-art results. Code will be available at https://github.com/alexhe101/FourierISP.

The thermal-to-visible (T2V) face translation task is essential for enabling face verification in low-light or dark conditions by converting thermal infrared faces into their visible counterparts. However, this task faces two primary challenges. First, the inherent differences between the modalities hinder the effective use of thermal information to guide RGB face reconstruction. Second, translated RGB faces often lack the identity details of the corresponding visible faces, such as skin color. To tackle these challenges, we introduce DiffTV, the first Latent Diffusion Model (LDM) specifically designed for T2V facial image translation with a focus on preserving identity. Our approach proposes a novel heterogeneous feature alignment strategy that bridges the modal gap and extracts both coarse-and fine-grained identity features consistent with visible images. Furthermore, a dual-stage condition injection strategy introduces control information to guide identity-preserved translation. Experimental results demonstrate the superior performance of DiffTV, particularly in scenarios where maintaining identity integrity is critical.

Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile, supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised denoisers, which learn only from single noisy images, solve the data collection problem. However, self-supervised denoising methods, especially blindspot-driven ones, suffer sizable information loss during input or network design. The absence of valuable information dramatically reduces the upper bound of denoising performance. In this paper, we propose a simple yet efficient approach called Blind2Unblind to overcome the information loss in blindspot-driven denoising methods. First, we introduce a global-aware mask mapper that enables global perception and accelerates training. The mask mapper samples all pixels at blind spots on denoised volumes and maps them to the same channel, allowing the loss function to optimize all blind spots at once. Second, we propose a revisible loss to train the denoising network and make blind spots visible. The denoiser can learn directly from raw noise images without losing information or being trapped in identity mapping. We also theoretically analyze the convergence of the revisible loss. Extensive experiments on synthetic and real-world datasets demonstrate the superior performance of our approach compared to previous work. Code is available at https://github.com/demonsjin/Blind2Unblind.