If you stuck a full-sized camera lens on a phone, it would be a monstrosity. Smartphones need to be small, compact, and somewhat durable, so they tend to utilize incredibly small camera sensors and lenses.

This teensy-weensy hardware creates several physical constraints. While a smartphone may have a 50MP sensor, the sensor’s size is actually quite small, meaning that less light can reach each pixel. This leads to reduced low-light performance and can introduce noise to an image.

Lens size is also important. Tiny camera lenses can’t bring in a ton of light, so you end up with a reduced dynamic range and, once again, reduced low-light performance. A tiny lens also means a small aperture, which cannot produce a shallow depth of field for background-blur or “bokeh” effects.

At a physical level, smartphones cannot take high-quality photos. Advancements in sensor and lens technology have greatly improved the quality of smartphone cameras, but the best smartphone cameras come from brands that utilize “computational photography.”

The best smartphone cameras come from Apple, Google, and Samsung—three leaders in software development. This is no coincidence. In order to push past the hardware constraints of smartphone cameras, these brands use “computational photography” to process and enhance photos.

Smartphones use multiple computational photography techniques to produce a high-quality image. Some of these techniques are predictable; a phone will automatically adjust the color and white balance of a photo, or it may “beautify” a subject by sharpening and brightening their face.

But the most advanced computational photography techniques go beyond simple image editing.

Take “stacking,” for instance. When you press the shutter button on your phone, it takes multiple images within the span of a few milliseconds. Each image is made with slightly different settings—some are blurry, some are overexposed, and some are zoomed in. All of these photos are combined to  produce an image with a high dynamic range, strong colors, and minimal motion blur.

Stacking is the key concept behind HDR photography, and it’s the starting point for a large number of computational photography algorithms. Night mode, for example, uses stacking to produce a bright nighttime image without a long exposure time (which would introduce motion blur and other problems).

And, as I mentioned earlier, smartphone cameras cannot produce a shallow depth of field. To get around this problem, most smartphones offer a portrait mode that uses software to estimate depth. The results are pretty hit or miss, especially if you have long or frizzy hair, but it’s better than nothing.

Some people believe that computational photography is “cheating,” as it misrepresents your smartphone camera’s capabilities and produces an “unrealistic” image. I’m not sure why this would be a serious concern. Computational photography is imperfect, but it allows you to take high-quality photos using low-quality hardware. In many cases, this brings you closer to a “realistic” and “natural” image with a sense of depth and dynamic range.

Some people are unhappy with computational photography. They argue that it misrepresents reality, and therefore, it must be bad! Cameras should give you the exact image that enters the camera’s lens—anything else is a lie!

Here’s the thing; every photograph contains some level of “fakery.” It doesn’t matter if the photo was shot on a phone, a DSLR camera, or a film camera.

Let’s look at the film photography process. Camera film is coated with a photosensitive emulsion. When the camera shutter opens, this emulsion is exposed to light, leaving an invisible chemical trace of an image. The film is dunked through a series of chemicals to produce a permanent negative, which is projected on an emulsion-lined paper to create a printed image (well, the photo paper also needs a chemical wash, but that’s the gist of it).

Every step in this process affects how an image looks. One brand of film may oversaturate reds and greens, while another brand may have a dull appearance. Darkroom chemicals may alter an image’s color or white balance. And printing an image to photo paper introduces even more variables, which is why many film labs use a reference sheet (or a computer) to dial in color and exposure.

Most people who owned a film camera were not professional photographers. They had no control over the printing process, and they certainly didn’t choose the chemical composition of their film. Doesn’t that sound familiar? Film manufacturers and photo labs were the “computational photography” of their day.

Reality is an important part of photography. Sometimes we want a photograph that accurately represents a moment in time, flaws and all. But more often than not, we ask our cameras to capture a good image, even in unfavorable circumstances—we ask for fakery.

This fakery requires technological advancements beyond the camera lens. And computational photography, despite its imperfections and marketing spin, is the technology we need right now.

That said, companies like Google, Apple, and Samsung need to be more transparent with their customers. We’re constantly bombarded by advertisements that stretch the truth, leading many people to believe that smartphones are comparable to full-sized or professional-grade cameras. This simply isn’t true, and until customers understand what’s going on, they’re going to keep getting mad about computational photography.