# #Dynamic Range

arxiv.org

### Characterization of 128x128 MM-PAD-2.1 ASIC: A Fast Framing Hard X-Ray Detector with High Dynamic Range

We characterize a new x-ray Mixed-Mode Pixel Array Detector (MM-PAD-2.1) Application Specific Integrated Circuit (ASIC). Using an integrating pixel front-end with dynamic charge removal architecture, the MM-PAD-2.1 ASIC extends the maximum measurable x-ray signal (in 20 keV photon units) to > 10$^{7}$ x-rays/pixel/frame while maintaining a low read noise across the full dynamic range, all while imaging continuously at a frame rate of up to 10 kHz. The in-pixel dynamic charge removal mechanism prevents saturation of the input amplifier and proceeds in parallel with signal integration to achieve dead-time-less measurements with incident x-ray rates of > 10$^{10}$ x-rays/pixel/s. The ASIC format consists of 128$\times$128 square pixels each 150 $\mu$m on a side and is designed to be 3-side buttable so as to tile large arrays. Here we use both laboratory x-ray sources and the Cornell High Energy Synchrotron Source (CHESS) to characterize two single ASIC prototype detectors for both low (single x-ray) and high incident flux detection. In the first detector the ASIC was solder bump-bonded to a 500 $\mu$m thick Si sensor for efficient detection of x-rays below ~20 keV, whereas the second detector used a 750 $\mu$m thick CdTe sensor for x-rays above $\sim$ 20 keV.
COMPUTERS
arxiv.org

### HDR-NeRF: High Dynamic Range Neural Radiance Fields

We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper. The radiance field encodes the scene radiance (values vary from 0 to +infty), which outputs the density and radiance of a ray by giving corresponding ray origin and ray direction. The tone mapper models the mapping process that a ray hitting on the camera sensor becomes a pixel value. The color of the ray is predicted by feeding the radiance and the corresponding exposure time into the tone mapper. We use the classic volume rendering technique to project the output radiance, colors, and densities into HDR and LDR images, while only the input LDR images are used as the supervision. We collect a new forward-facing HDR dataset to evaluate the proposed method. Experimental results on synthetic and real-world scenes validate that our method can not only accurately control the exposures of synthesized views but also render views with a high dynamic range.
COMPUTERS
arxiv.org

### NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images

Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis from a collection of posed input images. Like most view synthesis methods, NeRF uses tonemapped low dynamic range (LDR) as input; these images have been processed by a lossy camera pipeline that smooths detail, clips highlights, and distorts the simple noise distribution of raw sensor data. We modify NeRF to instead train directly on linear raw images, preserving the scene's full dynamic range. By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks. In addition to changing the camera viewpoint, we can manipulate focus, exposure, and tonemapping after the fact. Although a single raw image appears significantly more noisy than a postprocessed one, we show that NeRF is highly robust to the zero-mean distribution of raw noise. When optimized over many noisy raw inputs (25-200), NeRF produces a scene representation so accurate that its rendered novel views outperform dedicated single and multi-image deep raw denoisers run on the same wide baseline input images. As a result, our method, which we call RawNeRF, can reconstruct scenes from extremely noisy images captured in near-darkness.
COMPUTERS

### DJI Mavic 2 Pro VS Mavic 3 Pro | Should You Upgrade?

ELECTRONICS
Tony & Chelsea Northrup

### Sony a7 IV Image Quality Review: vs Canon R6, Sony a7 III, a9, a7R III

ELECTRONICS
Nathan Cool Photo

PHOTOGRAPHY
Gemini Connect

### DJI Action 2 - 4K 60FPS Test - How Long Can It Last?

ELECTRONICS
Filmmaker Central

ELECTRONICS
TrendHunter.com

### Style-Conscious Guitar Amps

The Amonito guitar amplifier is a stylish take on the classic piece of musician equipment that will provide both functionality and fashion for users to appreciate. The device is equipped with two vacuum tubes including the 12AX7 and the 12AU7, which are all running on high voltage to give the guitarist impressively dynamic range that isn't possible with digital processing alone. A series of strategically placed electronic switches make the unit one of the most versatile pieces of equipment on the market.
ELECTRONICS
nikonrumors.com

### Nikon Z9 dynamic range test

The camera uses a sensor that looks very similar to the one in the Z 7. That alone is a brave decision because up until now the top cameras for photojournalists from Nikon and others used special sensors tuned to high ISO performance. Personally, I’m very happy with that decision, because the gains at very high ISO values of sensors like the one for the D6 were relatively small and the loss of dynamic range at low ISO was relatively large. It looks like the sensor offers the best of both worlds – as did, in fact, the sensor of the Z 7 (II).
ELECTRONICS
iPhonedo

ELECTRONICS
MountMedia

### DJI Action 2 vs GoPro Hero 10 Image Quality, Stabilization, Overheating!

ELECTRONICS
Austin Guitar House

AUSTIN, TX
Artlist

ELECTRONICS