Image Type Water Meter Character Recognition Based on Embedded DSP

In the paper, we combined DSP processor with image processing algorithm and studied the method of water meter character recognition. We collected water meter image through camera at a fixed angle, and the projection method is used to recognize those digital images. The experiment results show that the method can recognize the meter characters accurately and artificial meter reading is replaced by automatic digital recognition, which improves working efficiency

Microbiota and bile acid profiles in retinoic acid-primed mice that exhibit accelerated liver regeneration.

Background & aimsAll-trans Retinoic acid (RA) regulates hepatic lipid and bile acid homeostasis. Similar to bile acid (BA), RA accelerates partial hepatectomy (PHx)-induced liver regeneration. Because there is a bidirectional regulatory relationship between gut microbiota and BA synthesis, we examined the effect of RA in altering the gut microbial population and BA composition and established their relationship with hepatic biological processes during the active phases of liver regeneration.MethodsC57BL/6 mice were treated with RA orally followed by 2/3 PHx. The roles of RA in shifting gut microbiota and BA profiles as well as hepatocyte metabolism and proliferation were studied.ResultsRA-primed mice exhibited accelerated hepatocyte proliferation revealed by higher numbers of Ki67-positive cells compared to untreated mice. Firmicutes and Bacteroidetes phyla dominated the gut microbial community (>85%) in both control and RA-primed mice after PHx. RA reduced the ratio of Firmicutes to Bacteroidetes, which was associated with a lean phenotype. Consistently, RA-primed mice lacked transient lipid accumulation normally found in regenerating livers. In addition, RA altered BA homeostasis and shifted BA profiles by increasing the ratio of hydrophilic to hydrophobic BAs in regenerating livers. Accordingly, metabolic regulators fibroblast growth factor 21, Sirtuin1, and their downstream targets AMPK and ERK1/2 were more robustly activated in RA-primed than unprimed regenerating livers.ConclusionsPriming mice with RA resulted in a lean microbiota composition and hydrophilic BA profiles, which were associated with facilitated metabolism and enhanced cell proliferation

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To further generate realistic faces, we propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Moreover, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively. Code will be made available upon publication.Comment: Chen and Tai contributed equally to this pape