The Reference-based Super-resolution (RefSR) super-resolves a low-resolution (LR) image given an external high-resolution (HR) reference image, where the reference image and LR image share similar viewpoint but with significant resolution gap (8×). Existing RefSR methods work in a cascaded way such as patch matching followed by synthesis pipeline with two independently defined objective functions, leading to the inter-patch misalignment, grid effect and inefficient optimization. To resolve these issues, we present CrossNet, an end-to-end and fullyconvolutional deep neural network using cross-scale warping. Our network contains image encoders, cross-scale warping layers, and fusion decoder: the encoder serves to extract multi-scale features from both the LR and the reference images; the cross-scale warping layers spatially aligns the reference feature map with the LR feature map; the decoder finally aggregates feature maps from both domains to synthesize the HR output. Using cross-scale warping, our network is able to perform spatial alignment at pixel-level in an end-to-end fashion, which improves the existing schemes [1,2] both in precision (around 2dB-4dB) and efficiency (more than 100 times faster).

Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we propose to utilize the exponentiated derivatives (with an exponent γ) of an observed image to generate a structure map when being amplified with γ > 1 and a texture map when being shrank with γ < 1. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents γ on the local derivatives. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model in an alternating minimization manner. Each sub-problem is transformed into a vectorized least squares regression with closed-form solution. Comprehensive experiments demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance estimation, low-light image enhancement, and color correction.

Calcium imaging is inherently susceptible to detection noise especially when imaging with high frame rate or under low excitation dosage. We developed DeepCAD, a selfsupervised learning method for spatiotemporal enhancement of calcium imaging without requiring any high signal-to-noise ratio (SNR) observations. Using this method, detection noise can be effectively suppressed and the imaging SNR can be improved more than tenfold, which massively improves the accuracy of neuron extraction and spike inference and facilitate the functional analysis of neural circuits.Calcium imaging enables parallel recordings of large neuronal ensembles in living animals [1][2][3][4] and offers a new possibility for deciphering information propagation, integration, and computation in neural circuits 5 . To obtain accurate neuron extraction and spike inference for downstream neuroscience analysis, high-SNR calcium imaging is desired. However, due to the paucity of fluorescence photons caused by low peak accumulations and fast dynamics of in vivo calcium transients 6,7 , calcium imaging is easy to be contaminated by detection noise (i.e. photon shot noise and electronic noise), especially in functional imaging where high temporal resolution is particularly important for analyzing neural activities 8 .To capture sufficient fluorescence photons for high-SNR calcium imaging, the most direct way is to use high excitation dosage, but concurrent photobleaching, phototoxicity 9,10 , and tissue heating 11 are detrimental for sample health and photosensitive biological processes, which limits the maximal excitation power for long-term in vivo imaging 12 . More effective .